Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
The C Users Journal
Mining the network value of customers
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
DualMiner: a dual-pruning algorithm for itemsets with constraints
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Mining knowledge-sharing sites for viral marketing
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
ExAMiner: Optimized Level-wise Frequent Pattern Mining with Monotone Constraints
ICDM '03 Proceedings of the Third IEEE International Conference on Data Mining
Maximizing the spread of influence through a social network
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Natural communities in large linked networks
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Group formation in large social networks: membership, growth, and evolution
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
Structure and evolution of online social networks
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
Social Capital in Friendship-Event Networks
ICDM '06 Proceedings of the Sixth International Conference on Data Mining
Finding tribes: identifying close-knit individuals from employment patterns
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
Cost-effective outbreak detection in networks
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
A framework for community identification in dynamic social networks
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
Collaboration over time: characterizing and modeling network evolution
WSDM '08 Proceedings of the 2008 International Conference on Web Search and Data Mining
Preferential behavior in online groups
WSDM '08 Proceedings of the 2008 International Conference on Web Search and Data Mining
Identifying the influential bloggers in a community
WSDM '08 Proceedings of the 2008 International Conference on Web Search and Data Mining
Optimal marketing strategies over social networks
Proceedings of the 17th international conference on World Wide Web
Influential nodes in a diffusion model for social networks
ICALP'05 Proceedings of the 32nd international conference on Automata, Languages and Programming
Fast shortest path distance estimation in large networks
Proceedings of the 18th ACM conference on Information and knowledge management
Characterizing the evolution of collaboration network
Proceedings of the 2nd ACM workshop on Social web search and mining
Detecting opinion leaders and trends in online social networks
Proceedings of the 2nd ACM workshop on Social web search and mining
Everyone's an influencer: quantifying influence on twitter
Proceedings of the fourth ACM international conference on Web search and data mining
Social Network Analysis and Mining for Business Applications
ACM Transactions on Intelligent Systems and Technology (TIST)
On the distinctiveness of tags in collaborative tagging systems
Proceedings of the International Conference on Web Intelligence, Mining and Semantics
User reputation in a comment rating environment
Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining
Sparsification of influence networks
Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining
FRINGE: a new approach to the detection of overlapping communities in graphs
ICCSA'11 Proceedings of the 2011 international conference on Computational science and its applications - Volume Part III
Information diffusion in social networks: observing and affecting what society cares about
Proceedings of the 20th ACM international conference on Information and knowledge management
Discovering leaders from social network by action cascade
Proceedings of the Fifth Workshop on Social Network Systems
A framework for exploring organizational structure in dynamic social networks
Decision Support Systems
Conversation retrieval for microblogging sites
Information Retrieval
Ranking User Influence in Healthcare Social Media
ACM Transactions on Intelligent Systems and Technology (TIST)
Finding trendsetters in information networks
Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining
Active microbloggers: identifying influencers, leaders and discussers in microblogging networks
SPIRE'12 Proceedings of the 19th international conference on String Processing and Information Retrieval
Exploring celebrity dynamics on Twitter
Proceedings of the 5th IBM Collaborative Academia Research Exchange Workshop
Expert Systems with Applications: An International Journal
Discovering content-based behavioral roles in social networks
Decision Support Systems
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We introduce a novel frequent pattern mining approach to discover leaders and tribes in social networks. In particular, we consider social networks where users perform actions. Actions may be as simple as tagging resources (urls) as in del.icio.us, rating songs as in Yahoo! Music, or movies as in Yahoo! Movies, or users buying gadgets such as cameras, handhelds, etc. and blogging a review on the gadgets. The assumption is that actions performed by a user can be seen by their network friends. Users seeing their friends' actions are sometimes tempted to perform those actions. We are interested in the problem of studying the propagation of such "influence", and on this basis, identifying which users are leaders when it comes to setting the trend for performing various actions. We consider alternative definitions of leaders based on frequent patterns and develop algorithms for their efficient discovery. Our definitions are based on observing the way influence propagates in a time window, as the window is moved in time. Given a social graph and a table of user actions, our algorithms can discover leaders of various flavors by making one pass over the actions table. We run detailed experiments to evaluate the utility and scalability of our algorithms on real-life data. The results of our experiments confirm on the one hand, the efficiency of the proposed algorithm, and on the other hand, the effectiveness and relevance of the overall framework. To the best of our knowledge, this the first frequent pattern based approach to social network mining.